Fuzzy Region Research Articles

A four-way decision-making approach using interval-valued fuzzy sets, rough set and granular computing: a new approach in data classification and decision-making

In this paper, a novel method is presented to solve the problems of classification and decision-making by employing the interval-valued fuzzy set, rough set and granular computing (GrC) concepts. The proposed method can classify the objects available in a system, called an interval-valued fuzzy decision table into the four distinct regions as interval-valued fuzzy-positive region, interval-valued fuzzy-negative region, interval-valued completely fuzzy region and interval-valued gray fuzzy region. These four regions constitute a new space for decision-making, which is termed as a four-way interval-valued decision space (4WIVDS). Based on the classified objects, various decision rules are generated from the distinct regions of the 4WIVDS. The study shows that the interval-valued gray fuzzy region has included most prominent decision rules. For taking precise level of decisions based on this particular region, this study utilizes the GrC to extract granular level of decision rules which are prominent by nature. The proposed 4WIVDS method is verified and validated with various benchmark datasets. Experimental results include statistical and comparison analyses which signify the efficiency of the proposed method in classifying the objects and generating the decision rules.

$H_{\infty }$ Observer Design for Fuzzy System With Immeasurable State Variables via a New Lyapunov Function

This paper is concerned with the design of $H_{\infty }$ observers for fuzzy system with immeasurable state variables via a new Lyapunov function. By decomposing the space of premise variables into crisp regions and fuzzy regions, piecewise fuzzy $H_{\infty }$ observers are established at different instants to cope with the challenges caused by the mismatch of the premise variables and exogenous disturbances. Based on the proposed new Lyapunov functions that depend on the different regions where the premise variables are located, sufficient conditions in terms of linear matrix inequalities are obtained to guarantee the stability and desired performance of the error systems. Finally, a simulation example is provided to show that the new design technology proposed in this paper achieves better $H_{\infty }$ performance than the existing design methods.

PSDSD-A Superpixel Generating Method Based on Pixel Saliency Difference and Spatial Distance for SAR Images.

Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local k-means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks.

The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network.

Human Lesion Detection Method Based on Image Information and Brain Signal

The brain is the largest and most complex structure in the central nervous system. It dominates all activities in the body, and the lesions in the human body are also reflected in the brain signal. In this paper, the image method is used to assist the brain signal to detect the human lesion. Due to the particularity of medical images, there is no common segmentation method for any medical image, and there is no objective standard to judge whether the segmentation is effective. Medical image segmentation technology is still a bottleneck restricting the development and the application of other related technologies in medical image processing. Based on the above reasons, this paper proposes an improved region growing algorithm based on the fuzzy theory and region growing algorithm. The algorithm is used to segment the medical images of the liver and chest X-ray of different human organs. The improved algorithm uses a threshold segmentation algorithm to assist in the automatic selection of seed points and improves the region growing rules, then morphological post-processing is used to improve the segmentation effect. The experimental results show that the improved region growing algorithm has better segmentation effect under two different organs, which proves that the algorithm has certain applicability, and its accuracy and segmentation quality are better than the traditional region growing algorithm. This algorithm combines the advantages of the threshold method and traditional region growing method. It is feasible in algorithm and has certain application value.

Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set

In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.

Fuzzy Region-Based Active Contours Driven by Weighting Global and Local Fitting Energy

contour model (ACM) has been a successful method for image segmentation. The existing ACMs poorly segment the images with intensity inhomogeneity or non-homogeneity, and the results highly depend on the initial position of the contour. To overcome these disadvantages, we proposed a fuzzy region-based active contour driven by weighting global and local fitting energy, wherein we propose a fuzzy region energy with local spatial image information, which has been proved convex and ensures the segmentation results independent of initialization, to motivate an initial evolving curve of pseudo level set function (LSF), followed by the pseudo LSF and further smoothed by an edge energy to accurately extract the object boundaries and maintain its distance feature. In addition, in the fuzzy region energy, instead of using the Euler-Lagrange equation to minimize the energy functional, we develop a more direct method to calculate the change of the fuzzy region energy. The experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models, and takes less running time. The code is available at: https://github.com/fangchj2002/FRAGL .

Modulation of Measles Virus NTAIL Interactions through Fuzziness and Sequence Features of Disordered Binding Sites

In this paper we review our recent findings on the different interaction mechanisms of the C-terminal domain of the nucleoprotein (N) of measles virus (MeV) NTAIL, a model viral intrinsically disordered protein (IDP), with two of its known binding partners, i.e., the C-terminal X domain of the phosphoprotein of MeV XD (a globular viral protein) and the heat-shock protein 70 hsp70 (a globular cellular protein). The NTAIL binds both XD and hsp70 via a molecular recognition element (MoRE) that is flanked by two fuzzy regions. The long (85 residues) N-terminal fuzzy region is a natural dampener of the interaction with both XD and hsp70. In the case of binding to XD, the N-terminal fuzzy appendage of NTAIL reduces the rate of α-helical folding of the MoRE. The dampening effect of the fuzzy appendage on XD and hsp70 binding depends on the length and fuzziness of the N-terminal region. Despite this similarity, NTAIL binding to XD and hsp70 appears to rely on completely different requirements. Almost any mutation within the MoRE decreases XD binding, whereas many of them increase the binding to hsp70. In addition, XD binding is very sensitive to the α-helical state of the MoRE, whereas hsp70 is not. Thus, contrary to hsp70, XD binding appears to be strictly dependent on the wild-type primary and secondary structure of the MoRE.

SLIC_SVM based leaf diseases saliency map extraction of tea plant

For the purpose of improving the extraction of tea plant leaf disease saliency map under complex backgrounds, a new algorithm combining SLIC (Simple Linear Iterative Cluster) with SVM (Support Vector Machine) is proposed in this paper. Firstly, super-pixel block is obtained by SLIC algorithm, significant point is detected by Harris algorithm, and fuzzy salient region contour is extracted by employing convex hull method. Secondly, the four-dimensional texture features of super-pixel blocks in salient regions and background areas are extracted, and then the classification map is obtained by classifying the super-pixel blocks with the help of SVM classifier. Lastly, the morphological and algebraic operations are implemented for repairing classified super-pixel blocks. As a result, one accurate saliency map of tea plant leaf disease image is obtained. Through testing based on 261 diseased images, the quality evaluation index, the accuracy, precision, recall and F-value are 98.5%, 96.8%, 98.6% and 97.7%, respectively. It demonstrates that the proposed method performs better than the other three SLIC-based algorithms in visual effects and quality assessment index. Such conclusion can be drawn that the proposed method can effectively extract tea plant leaf disease saliency map from complex background. Consequently, this research is expected to lay a good basis for the study of tea plant leaf disease identification. Last but not the least, the proposed method has good potential that extracts saliency map of crops or plants disease.

Contactless finger-vein verification based on oriented elements feature

Finger vein pattern has emerged as one of the most popular and promising biometric features for personal identity verification due to its uniqueness, stability, high distinguishability and non-invasive procedure. Many finger vein recognition algorithms have been proposed and successfully applied for identity authentication in the past decades. However, most of the previous methods usually only use the images captured by using a special fixed setting to avoid translation and rotation resulting from finger moving during image acquisition. Contactless finger vein recognition has recently begun to draw attention of researchers because of its user friendliness. Contactless vein images are captured under free conditions and usually have significant variations on rotations and translations. Conventional powerful recognition researches are not very effective for contactless vein images. Therefore, this paper proposed a new method based on finger vein patterns by employing region of interest extraction and oriented elements feature extraction scheme to effectively deal with non-ideal scenarios such as variations. A region of interest based on rotation rectified is employed to reduce the effects of rotation and translation during finger vein image capture. Then, the direction feature is drawn from the texture and steady orientation characteristics of finger vein images by using the magnitude and orientation of the gradient of points on finger vein lines in fuzzy division region. Using the extracted feature can effectively solve the problem of the geometric deformation. High accuracy has been obtained by the proposed scheme. Experimental results show that this new algorithm is effective and feasible in finger vein recognition system.

On identifying fuzzy knees in fuzzy multi-criteria optimization problems

This paper introduces and analyzes the idea of fuzzy knee in fuzzy multi-criteria optimization problems. The fuzzy decision feasible region of the problem is constructed under a fuzzy inequality relation that is defined with the help of same points in fuzzy geometry. Then, fuzzy criteria feasible region is obtained through the image of the fuzzy decision feasible region by the criteria-vector-valued mapping. For the constructed fuzzy criteria feasible region, we define fuzzy knee and then propose a method to capture the fuzzy knee regions, along with the complete fuzzy Pareto set. All the studied ideas and methodologies are supported with suitable examples and pictorial illustrations. An engineering application of the presented method is also given.

Accurate extraction of outermost biological characteristic curves in tooth preparations with fuzzy regions

BackgroundThe accuracy of extraction of biological characteristic curves in tooth preparations directly determines whether the tooth restorations and preparations are closely matched to allow appropriate adhesion. Ultimately, these will affect the success of the dental restoration surgery. In the process to obtain the tooth preparation, the dentist is required to grind the tooth manually and fuzzy regions may thus exist. Multiple feature curves with locally increased curvatures exist in these fuzzy regions, but only the outermost is preferred. The characteristic curve consists of points, some with and some without extreme curvature values. This study aims to extract an accurate biological characteristic curve. MethodThis challenging problem is mapped to the search of the minimum cost path for a graph, and is solved using the well-known A* algorithm. To identify the mapped graph, the outward direction coefficient is first introduced followed by the extremality coefficient node. Both of these coefficients ensure that the biological characteristic curve can be accurately extracted. ResultsThe conducted experiment demonstrated that the proposed algorithm can rapidly, accurately, and automatically obtain the outermost feature curve which passes through the fuzzy region of the tooth preparation. Additionally, the part of the biological characteristic curve related to the non-fuzzy region can also be accurately extracted. ConclusionsThe proposed algorithm significantly improves the accuracy of the extraction curve and the quality of the restoration design.

Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature

Identifying abnormality using breast mammography is a challenging task for radiologists due to its nature. A more consistent and precise imaging based CAD system plays a vital role in the classification of doubtful breast masses. In the proposed CAD system, pre-processing is performed to suppress the noise in the mammographic image. Then segmentation locates the tumor in mammograms using the cascading of Fuzzy C-Means (FCM) and region-growing (RG) algorithm called FCMRG. Features extraction step involves identification of important and distinct elements using Local Binary Pattern Gray-Level Co-occurrence Matrix (LBP-GLCM) and Local Phase Quantization (LPQ). The hybrid features are obtained from these techniques. The mRMR algorithm is employed to choose suitable features from individual and hybrid feature sets. The nominated feature sets are analysed through various machine learning procedures to isolate the malignant tumors from the benign ones. The classifiers are probed on 109 and 72 images of MIAS and DDSM databases respectively using k-fold (10-fold) cross-validation method. The enhanced classification accuracy of 98.2% is achieved for MIAS dataset using hybrid features classified by Decision Tree. Whereas 95.8% accuracy is obtained for DDSM dataset using KNN classifier applied on LPQ features.

A four-way decision-making system for the Indian summer monsoon rainfall

Due to non-stationary nature of Indian summer monsoon rainfall (ISMR), analysis of its patterns and behaviors is a very tedious task. Advance prediction and behaviors play a significant role in various domains. Literature review reveals that researchers’ works are limited to design predictive models but not on inherited patterns and behaviors for the ISMR. In this study, a novel method based on the hybridization of two computational techniques, viz., fuzzy and rough sets is proposed for patterns and behaviors. The proposed method initially classifies the information into the four distinct regions, as fuzzy positive region, fuzzy negative region, completely fuzzy region, and gray fuzzy region. Based on four regions, four different patterns of decision rules are explored. Further, a method is discussed to represent such decision rules in terms of graph, which helps to analyze the patterns of ISMR by discovering new knowledge. The proposed method is validated by performing various statistical analyses.

An outcome-based process optimization model using fuzzy-based association rules

PurposeThe purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment.Design/methodology/approachThis model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes.FindingsThe new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome.Research limitations/implicationsThe proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results.Originality/valueRecent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

Adaptive weighted fuzzy region based optimization for brain MR image segmentation

The recent trends in medical image segmentation and analysis are often used in many real world applications for analyzing different objects of interest. Analyzing the brain Magnetic Resonance (MR) images is found to be difficult task because of the existence of intensity non-uniformity. Although numerous models have been proposed to handle brain MR image segmentation, it is still a challenge to effectively approximate the Intensity Non-Uniformity (INU) and improve greater segmentation accuracy. Hence, an integrated energy minimization approach, namely adaptive weighted fuzzy region based optimization algorithm is developed for brain MR image segmentation. These adaptive fuzzy regions are iteratively weighted to estimate their membership values assigned to each pixel with respect to energy. Also, the optimal weighting parameter, membership values to each region, and bias fields are iteratively estimated and updated. Further, this algorithm is compared with the recent energy minimization approaches in simulated brain MR image dataset. The results of the quantitative evaluations demonstrate that the proposed algorithm gives more reliable segmentation and better accuracy in spite of initialization, noise, and intensity non-uniformity.

Radiomics based detection and characterization of suspicious lesions on full field digital mammograms

Background and objectiveEarly detection is the important key to reduce breast cancer mortality rate. Detecting the mammographic abnormality as a subtle sign of breast cancer is essential for the proper diagnosis and treatment. The aim of this preliminary study is to develop algorithms which detect suspicious lesions and characterize them to reduce the diagnostic errors regarding false positives and false negatives. MethodsThe proposed hybrid mechanism detects suspicious lesions automatically using connected component labeling and adaptive fuzzy region growing algorithm. A novel neighboring pixel selection algorithm reduces the computational complexity of the seeded region growing algorithm used to finalize lesion contours. These lesions are characterized using radiomic features and then classified as benign mass or malignant tumor using k-NN and SVM classifiers. Two datasets of 460 full field digital mammograms (FFDM) utilized in this clinical study consists of 210 images with malignant tumors, 30 with benign masses and 220 normal breast images that are validated by radiologists expert in mammography. ResultsThe qualitative assessment of segmentation results by the expert radiologists shows 91.67% sensitivity and 58.33% specificity. The effects of seven geometric and 48 textural features on classification accuracy, false positives per image (FPsI), sensitivity and specificity are studied separately and together. The features together achieved the sensitivity of 84.44% and 85.56%, specificity of 91.11% and 91.67% with FPsI of 0.54 and 0.55 using k-NN and SVM classifiers respectively on local dataset. ConclusionsThe overall breast cancer detection performance of proposed scheme after combining geometric and textural features with both classifiers is improved in terms of sensitivity, specificity, and FPsI.

Level set evolution of biomedical MRI and CT scan images using optimized fuzzy region clustering

In recent years, image segmentation based on level set formulations was encompassed by proficiently implementing various image processing applications, especially in case of reformative models for medical image segmentation. Existing level set methods under this criterion are not up to the mark in terms of noise, weak boundaries, and inhomogeneity. In this paper, we put forward a new formulation for selective segmentation with level set model using optimised fuzzy region clustering and implementation is carried out in two stages. In first, optimised fuzzy region clustering (OFR) is employed in which particle swarm optimisation technique is utilised at the initialisation of FCM clustering to obtain optimal cluster centres and then it is incorporated with region competition. While in second, implementing OFR clustering using level set methods. The proposed formulation is successfully implemented on some contemporary medicinal images. It is capable to identify the weak boundaries and segment the desired components of an image such as white substance, grey substance, cerebrospinal fluid, bone sarcoma, breast cancer and Alzheimer’s disease, etc. The visual results and quantitative analysis show its effectiveness, superiority and accuracy over the existing formulations.

Generalized Point Estimators for Fuzzy Multivariate Data

Data analysis methods are necessary tools in evaluating and better understanding the information of interest. However, there are limitations in applying standard statistical methods to specific data analyses. The data obtained from different sources are often clouded by imprecision and uncertainty. To overcome this problem, data analysis methods have to be generalized to capture the data uncertainty through statististical methods for fuzzy data. The existing methods are based on the extension principle or require other generalized procedures, such as the calculation of statistics, the extimation of parameters, and the construction of fuzzy confidence regions. The development of these methods to evaluate univariate data has been flourished. However, to solve complex real-world problems, these methods have to be extended and generalized to handle multivariate fuzzy data. In this research, the methods of generalized point estimators, i.e. sample mean, variance-covariance, and correlation coefficient, are extended for the multivariate case through concepts of fuzzy vector and combined fuzzy sample.

Determining Topological Relations of Fuzzy Spatiotemporal Regions Over Time in XML

Determining topological relations is one of the most important operations on fuzzy spatiotemporal data, which has an important significance in reasoning. In order to determine topological relations, a considerable amount of approaches have been proposed. Those efforts are quite efficient for determining discrete topological relations. But for continuous determining topological relations, the previous approaches often suffer from insufficient extensions and high cost, which significantly decrease the performance. To overcome this limitation, in this paper, we model topological relations between fuzzy spatiotemporal regions over time in XML and propose an algorithm to identify topological relations. Our approach adopts a combination of superscript and subscript so that continuous topological relations can be represented as unique. More importantly, determining topological relations in XML can reduce execution time rather than a traditional database, since XML has structural advantages and can avoid complex extensions. Finally, we run experiments to validate our approach and show its significant superiority.